Correction of reaction rules databases by active learning

ABSTRACT

A system and method for reaction rules database correction. The method includes receiving a user-input correction to a first reaction rule in a reaction rules database, and locating a second reaction rule in the reaction rules database that is similar to the first reaction rule. The method also includes calculating a correctness score for the second reaction rule, and determining that the correctness score for the second reaction rule is below a threshold correctness score. Additionally, the method includes presenting, in response to the determining that the correctness score for the second reaction rule is below the threshold correctness score, the second reaction rule to a user, receiving a user-input correction to the second reaction rule, and updating the reaction rules database to include the user-input correction to the second reaction rule.

BACKGROUND

The present disclosure relates to machine learning and more specificallyto correcting databases of chemical reaction rules by active learningmethods.

Chemical databases contain information about chemical compounds,reactions, thermophysical data, etc. This information can be manuallyinput, or automatically gathered from a variety of text and/or imagesources, such as chemical literature, textbooks, web pages, etc.Chemical databases are used for a number of purposes. For example, thesedatabases can be used to search for topics in scientific literature orchemical structures and substructures. Chemical databases can also beused to predict the products of theoretical reactions, generatetheoretical structures, retrosynthetically design syntheses of targetcompounds, predict the properties of compounds, etc.

SUMMARY

Various embodiments are directed to a method of reaction rule databasecorrection. The reaction rules database can include reaction rulesextracted from sources such as scientific articles, books, and patents.The method can include receiving a first user-input correction to afirst reaction rule in a reaction rules database, locating a secondreaction rule in the reaction rules database that is similar to thefirst reaction rule, and calculating a correctness score for the secondreaction rule, wherein the correctness score is a measure of how similarthe second reaction rule is to the first reaction rule. The method canalso include determining that the correctness score for the secondreaction rule is below a threshold correctness score, and in response tothe determining that the correctness score for the second reaction ruleis below the threshold correctness score, presenting the second reactionrule to a user. Further, the method can include receiving a seconduser-input correction to the second reaction rule, and updating thereaction rules database to include the second user-input correction tothe second reaction rule. In some embodiments, the first reaction ruleand the second reaction rule have incorrect bonds. Presenting the secondreaction rule to the user can include automatically opening a new windowof a computer-assisted synthetic design program user interface.Additionally, an alert can be generated when the correctness score forthe second reaction rule is below the threshold correctness score.

The method can also include locating an additional reaction rule that issimilar to the second reaction rule, calculating a correctness score forthe additional reaction rule, determining that the correctness score forthe additional reaction rule is below the threshold correctness score,and in response to determining that the correctness score for theadditional reaction rule is below the threshold correctness score,presenting the additional reaction rule to the user. The method can alsoinclude receiving a user-input correction to the additional reactionrule, and updating the reaction rules database to include the user-inputcorrection to the additional reaction rule. Further, the method caninclude receiving a user-input confidence level for the user-inputcorrection to the second reaction rule, determining that the confidencelevel is below a threshold confidence level, and in response todetermining that the confidence level is below the threshold confidencelevel, presenting the second reaction rule to a second user. Uponreceiving confirmation of the second reaction rule from the second user,the reaction rules database can be updated to include the confirmation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram illustrating a generic example ofretrosynthetic analysis, according to some embodiments of the presentdisclosure.

FIG. 2 is a flow diagram illustrating a process of correcting a reactionrules database, according to some embodiments of the present disclosure.

FIG. 3 is a block diagram illustrating a reaction rules correctionsystem environment, according to some embodiments of the presentdisclosure.

FIG. 4 is a block diagram illustrating a computer system, according tosome embodiments of the present disclosure.

FIG. 5 is a block diagram illustrating a cloud computing environment,according to some embodiments of the disclosure

FIG. 6 is a block diagram illustrating abstraction model layers,according to some embodiments of the disclosure.

DETAILED DESCRIPTION

Chemical databases are used to store chemical information, and tosupplement the knowledge and strategies of scientists and engineers.These databases contain information relating to categories such aschemical compounds, chemical reactions, reaction rules, thermophysicaldata etc. This information is gathered from a variety of text and/orimage sources, such as chemical literature, patents, textbooks, webpages, etc. This information can be manually input or automaticallypopulated based on the gathered information. One example of a chemicaldatabase is the Beilstein database, which stores experimentally verifiedinformation about chemical reactions and substances found in scientificliterature ranging from 1771 to the present day. The Beilstein databasebegan as a handbook of organic chemistry in 1881, but was laterconverted into an electronic database. Today, there are a number ofliterature databases that contain structures and other chemicalinformation from scientific articles, supplementary materials, andpatents (e.g., Reaxys by Elsevier, Scopus by Elsevier, ChemSpider by theRoyal Society of Chemistry, SciFinder by Chemical Abstract Service(CAS), Science and Technical Information Network (STN International) byCAS, etc.).

Additional examples of chemical databases can include crystallographicdatabases, such as the Crystallography Open Database (COD), the ResearchCollaboratory for Structural Bioinformatics' Protein Data Bank (RCSBPDB), the Open Access Crystal Morphology Database (CMD), etc. Thesedatabases store crystal structures of and/or morphological informationabout minerals, metals and alloys, organic compounds, nucleic acids,biological macromolecules, polymers, organometallic compounds, etc.Crystallographic databases are typically relational databases, and canbe searched by keywords, physical properties, chemical elements, partialstructures, morphology, compound names, lattice parameters, etc.Additional examples of databases that contain molecular structureinformation can include the National Institute of Health PubChem,commercial databases such as eMolecules and The ChemExper ChemicalDirectory by ChemExper, the New York University Library of 3-D MolecularStructures, the Environmental Protection Agency's DistributedStructure-Searchable Toxicity (DSSTox) Database Network, etc.

Databases that store thermodynamic and thermophysical data are anothercategory of chemical databases. For example, the Dortmund Data Bank(DDB) stores experimental data collected from more than 92,000scientific articles, books, private communications, company reports,patents, theses, conference presentations, etc. Additional examples ofchemical databases can include nuclear magnetic resonance (NMR) spectradatabases, reaction databases, reaction rules databases, etc. Reactionrules databases store reaction rules (e.g., fundamental steps in achemical reaction), which are based on information such as that storedin the aforementioned chemical databases. This is discussed in greaterdetail below.

The information collected in chemical databases is extensive. Forexample, the literature database Reaxys has, as of October 2016, morethan 105 million chemical compounds, 45 million chemical reactions, and500 million experimental facts. This information is gleaned from over16,000 chemistry-related periodicals, patents from all major worldpatent offices, and approximately 450 scientific journals and textbookspublished since 1771. The data stored in chemical databases can be usedin a variety of applications other than providing searchable knowledgeto users. For example, the databases can provide data used to predictthe outcome of a reaction when a user enters starting materials and/orreaction conditions. Information from these databases can also be usedto predict properties of compounds based on their structures. Forexample, a user can enter a structure of an existing or theoreticalcompound into a prediction program. The program then determines thelikely properties of the compound based on fundamental principles and/orexperimental information about similar structures and materials.

Another application of chemical database information is inretrosynthetic analysis. Retrosynthetic analysis is a method ofdetermining possible synthetic paths to a target compound. In the past,this strategy relied solely on the background knowledge and reasoningskills of human scientists. For example, a scientist attempting todesign a new drug may decide that adding a hydroxyl functional group tothe structure of an existing drug that is an organic compound couldprovide beneficial new properties. The scientist might approach this byconsidering reactions that can add hydroxyl groups to organic compounds.The scientist could then narrow these reactions down to known reactionsinvolving organic compounds with structures similar to the existingdrug. The reaction parameters could be narrowed still further byconsidering practicalities such as availability and/or safety ofstarting materials and solvents, reaction time, reaction yield,difficulty of techniques, access to equipment, number of syntheticsteps, etc.

However, even with the amount of information available to the modernscientist through chemical databases, this approach to synthesizing anew drug is impractical. Designing a new drug takes an average of 12years and $800 million per drug. Additionally, the number of theoreticalcandidates for a single drug can range from 5,000-10,000 compounds, withapproximately 250 compounds showing promise. The time, effort, andmaterials required to test all of these compounds result in asignificant portion of the cost of developing a new drug. Further, evenwhen a candidate compound is selected to enter the clinical phase, thechance that it will receive approval from the Food and DrugAdministration (FDA) is about one in ten. Therefore, strategies thatincrease the efficiency of drug design and discovery are of interesttoday. One strategy that can be employed is computer-assisted synthesisdesign (CASD), which employs retrosynthetic analysis carried out usingmachine learning techniques.

Retrosynthetic analysis carried out using machine learning combined withthe information available in chemical databases offers an important toolfor modern chemical synthesis. A target compound is entered into a CASDprogram, which then uses machine learning techniques to quicklydetermine possible synthetic pathways to a new compound from chemicaldatabase information and, in particular, chemical reaction rulesdatabases. Reaction rules are a type of abstracted chemical reaction,and are discussed in greater detail below. Using machine learningtechniques allows a large number of candidate compounds to besynthesized and tested, speeding the process of drug discoveryconsiderably. Further, it should be noted that retrosynthetic analysiscan aid in uncovering synthetic approaches to forming compounds otherthan drugs. Examples of these compounds can include polymers, polymeradditives, catalysts, pesticides, dyes, artificial flavorings andsweeteners, compounds used in fundamental research, peptidomimetics,synthetic proteins, nanostructures, artificial genes, etc.

In the past, CASD programs relied upon manually input chemicalinformation and reaction rules. Therefore, retrosynthetic analysis waslimited by the pace and knowledge of humans. However, reaction rules cannow be extracted by automatic methods. This extraction employs naturallanguage processing and image processing to extract reaction examples(i.e., known compound syntheses) from sources such as those discussedabove with respect to various chemical databases. The known reactionsare generalized into reaction rules by first identifying reaction coresof the compound substructures. This is carried out using atom-to-atommapping between reactants and products in an example reaction. Thereaction core consists of substructures that have mapped atoms with thesame attributes in both the reactants and products. The reaction core isthen extended to include relevant neighboring functional groups. Then,similar reaction cores from different example reactions are groupedtogether, and combined to form generalized reaction cores. A reactionrule is then completed. The reaction rule represents the generalizedreaction core with ranges for each generalized property across theexample reactions.

This reaction rule extraction method provides vast numbers of reactionrules that are stored in reaction rules databases, and can be used toretrosynthetically generate possible pathways to target compounds.However, automatic reaction rule extraction results in a significantnumber of errors, particularly when the number of example reactions isinadequate for forming a reliable generalized core. Corrections can bemade by human users, but the great number of reaction rules makes itextremely difficult for human users to efficiently locate the errors.Therefore, updates to reaction rules databases to correct errors inreaction rules cannot rely solely on human error detection. A method forimproving the efficiency of reaction rule correction by active learningtechniques is disclosed herein. Active learning is a type ofsemi-supervised machine learning in which a learning algorithminteractively queries a user by requesting labels for selected data.This is discussed in greater detail below.

FIG. 1 is a schematic diagram illustrating a generic example ofretrosynthetic analysis. A target compound 105 is entered into a CASDprogram. The target compound 105 is entered as a compound name, achemical structure, and/or a molecular fingerprint for identifying atarget compound. Examples of computer-readable formats in which achemical structure of the target compound 105 can be entered can includeChemical Markup Language (CML), SYBYL line notation (SLN), simplifiedmolecular-input line-entry system (SMILES) notation, Ghemical format,Crystallographic Image File (CIF) format, Protein Data Bank (PDB) fileformat, XYZ file format, density functional theory (DFT) calculation,etc. The molecular fingerprint of the target compound 105 is representedby bit strings that summarize molecular information, such ascombinatorial features or information about functional groups.

The CASD program locates compounds with known synthesis pathways thatare similar to the target compound 105. For example, similar compoundscan be located based on Jaccard similarity, descriptive networkproperties and graph theory, chemical semantic measures, autocorrelationpolarizability, etc. In some embodiments, techniques for filtering thesimilarity search results can be used, such as succinct multibit treesearching or succinct interval splitting tree algorithm searching.

The CASD program then locates a reaction rule for synthesizing eachcompound in the set of similar compounds, and applies the reaction ruleto the target compound 105 in order to generate precursors. The programalso determines whether the precursors are starting materials (e.g.,compounds that are commercially available or have commonly knownsyntheses). If a precursor is not a starting material, the process isrepeated to find reaction rules for synthesizing the precursor. Thesynthetic pathway to the target compound 105 is considered complete whenall precursors are starting materials.

In process 100, two reaction rules, Rule 110 and Rule 120, thatrepresent synthetic pathways to the target compound A 105 are found.Rule 110 is a reaction between compound B and compound C. Compound B isillustrated in a box with a bold line to indicate that it is a startingmaterial, and compound C is illustrated in a box with a dashed line toindicate that it must be synthesized. Rule 120 is a reaction to form thetarget compound 105 from compounds D, E, and F, each of which must besynthesized. The synthetic pathway that uses Rule 110 is selected as thesimplest path because compound B is a starting material.

However, a synthetic path to compound C must then be determined. Again,two reaction rules are found. Rule 130 is a reaction to form compound Cfrom compounds G and H, and Rule 140 is a reaction to form compound Cfrom compounds I and J. Of these, only compound G is a startingmaterial. Therefore, Rule 130 is applied, and two reaction rules for asynthetic pathway to compound H are found. Rule 150 is a reactionbetween two compounds that must be synthesized, compounds K and L, andRule 160 is a reaction between three starting materials, compounds M, N,and O. Rule 160 is applied because it begins with starting materials.Therefore, a generalized synthetic pathway 170 to the target compound105 can be constructed from Rules 110, 130, and 160.

It should be noted that the example illustrated in FIG. 1 is asimplified example, and that the number of reaction rules located in adatabase for one compound can be extremely large (e.g., hundreds,thousands, tens of thousands, etc.). Further, factors other than thequickest path to a commercially available compound are often taken intoconsideration. For example, Rule 130 was chosen in this example becauseit leads directly to a starting material (compound B). Rules forgenerating compounds I and J from Rule 140 are not shown. However, itcould be that compounds I and J have facile syntheses, and that compoundG is commercially available, but prohibitively expensive. In a case suchas this, continuing the path begun in Rule 140 may be more practicalthan that of Rule 130, despite requiring additional steps. Variations inreaction parameters are discussed in greater detail above.

FIG. 2 is a flow diagram illustrating a process 200 of correcting areaction rules database. The process begins when at least one user-inputreaction rule correction is received. This is illustrated at step 210. Auser locates at least one reaction rule error while using a CASDprogram. Examples of reaction rule errors can include incorrect atoms,incorrect bonds, incorrect stereochemistry, etc. Further, reaction rulescan include information about reaction temperatures, toxicity ofcompounds, reaction yields, reaction speed, etc., and a user mayencounter mistakes in this information as well. Based on the user'sknowledge of reaction rules, the user inputs a corrected reaction rule,and the corrected rule is stored in the reaction rules database.However, in some embodiments the user can flag an error without enteringa corrected rule. Additionally, the user can delete an incorrectreaction rule without entering a corrected rule. Herein, any update to apotentially incorrect reaction rule entered by a user is referred to asa correction (e.g., entering a corrected rule, deleting an incorrectrule, editing a rule, assigning a confidence level marking, flagging anerror, etc.).

Once the user-input rule correction has been received, a similaritysearch is carried out to determine whether there are reaction rules inthe database that are similar to the corrected rule. This is illustratedat step 220. If a reaction rule in the database is similar to the rulethat the user corrected, it is possible that the located similar rule isincorrect as well. The similarities of the reaction rules arerepresented by correctness scores. A high correctness score indicateslow similarity between a reaction rule in the database and the correctedreaction rule. Reaction rules with correctness scores below a thresholdcorrectness score are considered to have substantial similarity to thecorrected reaction rules, and thus are flagged as potential errors.

The threshold correctness score can be preset or entered by a user. Insome embodiments the threshold correctness score can be adjusted by theuser in order to raise or lower the number of presented potentialerrors. There can be more than one level of threshold correctness score(e.g., a low threshold and a high threshold), which can indicatedifferent levels of probability of a rule being incorrect.

If no potential errors (i.e., reaction rules with correctness scoresbelow the threshold correctness score) are located, the process ends.However, if one or more potential errors are located, the potentialerrors are presented to the user. This is illustrated at step 230. Insome embodiments, potential errors are displayed to the user accordingto their correctness score (e.g., from lowest to highest correctnessscore). Additionally, the similar rules can be automatically displayedon a CASD program user interface while the user operates the CASDprogram. For example, a new window displaying the potential errors canautomatically open once the similar rules have been located. However,the similar reaction rules can also be stored, and accessed by opening anew window or clicking on a link that will take the user to a display ofthe potential errors. Additionally, in some embodiments an alert can betriggered when potential errors are located. Examples of alerts caninclude sounds, pop-up messages, emails, short message service (‘SMS’ ortext) messages, flashing lights, force or haptic feedback, electricimpulses, etc.

The potential errors are reviewed by a user who specifies whether theserules are in fact errors. This is illustrated at step 240. The reactionrules can be classified as either true or false. If the user indicatesthat the potential error is a valid reaction rule (i.e., true), theprocess ends. However, if the user determines that a displayed reactionrule is incorrect (i.e., false), the user confirms the error and entersa correction. The process then returns to step 210 with the newlyentered correction.

In some embodiments, the user assigns a confidence level to a reactionrule. For example, a user may be unsure whether a reaction rulepresented as a potential error is correct. If so, the user can mark therule as incorrect with a high, medium, or low confidence level. Itshould also be noted that there can be more than three levels, and thatthe levels can be expressed in a variety of ways (e.g., words, letters,percentages, numbers on a scale, colors, icons, etc.). The user can alsoattach a confidence level to a correction. The confidence level can beused to adjust the search rankings of the reaction rules. Additionally,if a user indicates less than complete confidence in a reaction rule(e.g., a confidence level below a confidence level threshold), anotheruser could be asked for a second confirmation. If the second userconfirms that the rule is incorrect, the reaction rules database isupdated to increase the confidence level. Further, the second user canalso enter a confidence level in some embodiments.

FIG. 3 is a block diagram illustrating a reaction rules correctionenvironment 300. A user enters a target compound 315 into a reactionrules correction module 320 via a user interface 310. The targetcompound 315 is entered as a compound name, a chemical structure, and/ora molecular fingerprint for identifying a target compound. The targetcompound 315 is received by a retrosynthetic analysis component 330. Theretrosynthetic analysis component 330 searches a reaction rules database340, and locates reaction rules 345 that provide potential syntheticpathways to the target compound 315. The process of carrying outretrosynthetic analysis on a target compound is discussed in greaterdetail with respect to FIG. 1.

The reaction rules 345 are presented to the user via the user interface310. If the user identifies an error in a reaction rule 345, the userenters a correction 355 (e.g., marks the rule “false”). Corrections arediscussed in greater detail with respect to FIG. 2. The user-inputcorrection 355 is received by a machine learning component 360 in thereaction rules correction module 320. The machine learning component 360locates one or more reaction rules within the reaction rules database340 that are similar to the reaction rule that was marked false by theuser. These reaction rules are given correctness scores based on theirsimilarity to the user-corrected reaction rule, and it is determinedwhether the rules have correctness scores below a threshold correctnessscore.

Reaction rules with correctness scores below the threshold correctnessscore (i.e., rules that may contain errors) are referred to as potentialerrors 365, and are displayed to the user via the user interface 310.The user confirms whether the potential errors 365 are incorrect, andenters another user-input correction 355. If the potential errors 365are confirmed to be incorrect by the user, the machine learningcomponent 360 updates the reaction rules database 340 to include theuser-input correction 355. Examples of active learning techniques themachine learning component 360 can employ to carry out databasecorrections can include support vector machine (SVM) algorithms,graph-based active learning methods, generative adversarial activelearning methods, active multi-relational learning algorithms, etc. Themachine learning component 360 uses the user-input corrections 355 tolearn functions for predicting the correctness of additional reactionrules.

FIG. 4 is a high-level block diagram illustrating an exemplary computersystem 400 that can be used in implementing one or more of the methods,tools, components, and any related functions described herein (e.g.,using one or more processor circuits or computer processors of thecomputer). In some embodiments, the major components of the computersystem 400 comprise one or more processors 402, a memory subsystem 404,a terminal interface 412, a storage interface 416, an input/outputdevice interface 414, and a network interface 418, all of which can becommunicatively coupled, directly or indirectly, for inter-componentcommunication via a memory bus 403, an input/output bus 408, businterface unit 407, and an input/output bus interface unit 410.

The computer system 400 contains one or more general-purposeprogrammable central processing units (CPUs) 402-1, 402-2, and 402-N,herein collectively referred to as the CPU 402. In some embodiments, thecomputer system 400 contains multiple processors typical of a relativelylarge system; however, in other embodiments the computer system 400 canalternatively be a single CPU system. Each CPU 402 may executeinstructions stored in the memory subsystem 410 and can include one ormore levels of on-board cache.

The memory 404 can include a random-access semiconductor memory, storagedevice, or storage medium (either volatile or non-volatile) for storingor encoding data and programs. In some embodiments, the memory 404represents the entire virtual memory of the computer system 400, and mayalso include the virtual memory of other computer systems coupled to thecomputer system 400 or connected via a network. The memory 404 isconceptually a single monolithic entity, but in other embodiments thememory 404 is a more complex arrangement, such as a hierarchy of cachesand other memory devices. For example, memory may exist in multiplelevels of caches, and these caches may be further divided by function,so that one cache holds instructions while another holds non-instructiondata, which is used by the processor or processors. Memory can befurther distributed and associated with different CPUs or sets of CPUs,as is known in any of various so-called non-uniform memory access (NUMA)computer architectures. The memory 404 also contains a reaction rulescorrection module 320, which is discussed in greater detail with respectto FIG. 3.

These components are illustrated as being included within the memory 404in the computer system 400. However, in other embodiments, some or allof these components may be on different computer systems and may beaccessed remotely, e.g., via a network. The computer system 400 may usevirtual addressing mechanisms that allow the programs of the computersystem 400 to behave as if they only have access to a large, singlestorage entity instead of access to multiple, smaller storage entities.Thus, though storage management system 120 is illustrated as beingincluded within the memory 404, components of the memory 404 are notnecessarily all completely contained in the same storage device at thesame time. Further, although these components are illustrated as beingseparate entities, in other embodiments some of these components,portions of some of these components, or all of these components may bepackaged together.

In an embodiment, the reaction rules correction module 320 includesinstructions that execute on the processor 402 or instructions that areinterpreted by instructions that execute on the processor 402 to carryout the functions as further described in this disclosure. In anotherembodiment, the reaction rules correction module 320 is implemented inhardware via semiconductor devices, chips, logical gates, circuits,circuit cards, and/or other physical hardware devices in lieu of, or inaddition to, a processor-based system. In another embodiment, thereaction rules correction module 320 includes data in addition toinstructions.

Although the memory bus 403 is shown in FIG. 4 as a single bus structureproviding a direct communication path among the CPUs 402, the memorysubsystem 410, the display system 406, the bus interface 407, and theinput/output bus interface 410, the memory bus 403 can, in someembodiments, include multiple different buses or communication paths,which may be arranged in any of various forms, such as point-to-pointlinks in hierarchical, star or web configurations, multiple hierarchicalbuses, parallel and redundant paths, or any other appropriate type ofconfiguration. Furthermore, while the input/output bus interface 410 andthe input/output bus 408 are shown as single respective units, thecomputer system 400 may, in some embodiments, contain multipleinput/output bus interface units 410, multiple input/output buses 408,or both. Further, while multiple input/output interface units are shown,which separate the input/output bus 408 from various communicationspaths running to the various input/output devices, in other embodimentssome or all of the input/output devices may be connected directly to oneor more system input/output buses.

The computer system 400 may include a bus interface unit 407 to handlecommunications among the processor 402, the memory 404, a display system406, and the input/output bus interface unit 410. The input/output businterface unit 410 may be coupled with the input/output bus 408 fortransferring data to and from the various input/output units. Theinput/output bus interface unit 410 communicates with multipleinput/output interface units 412, 414, 416, and 418, which are alsoknown as input/output processors (IOPs) or input/output adapters (IOAs),through the input/output bus 408. The display system 406 may include adisplay controller. The display controller may provide visual, audio, orboth types of data to a display device 405. The display system 406 maybe coupled with a display device 405, such as a standalone displayscreen, computer monitor, television, or a tablet or handheld devicedisplay. In alternate embodiments, one or more of the functions providedby the display system 406 may be on board a processor 402 integratedcircuit. In addition, one or more of the functions provided by the businterface unit 407 may be on board a processor 402 integrated circuit.

In some embodiments, the computer system 400 is a multi-user mainframecomputer system, a single-user system, or a server computer or similardevice that has little or no direct user interface, but receivesrequests from other computer systems (clients). Further, in someembodiments, the computer system 400 is implemented as a desktopcomputer, portable computer, laptop or notebook computer, tabletcomputer, pocket computer, telephone, smart phone, network switches orrouters, or any other appropriate type of electronic device.

It is noted that FIG. 4 is intended to depict the representative majorcomponents of an exemplary computer system 400. In some embodiments,however, individual components may have greater or lesser complexitythan as represented in FIG. 4, Components other than or in addition tothose shown in FIG. 4 may be present, and the number, type, andconfiguration of such components may vary.

In some embodiments, the data storage and retrieval processes describedherein could be implemented in a cloud computing environment, which isdescribed below with respect to FIGS. 5 and 6. It is to be understoodthat although this disclosure includes a detailed description on cloudcomputing, implementation of the teachings recited herein are notlimited to a cloud computing environment. Rather, embodiments of thepresent invention are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 5, illustrative cloud computing environment 500 isdepicted. As shown, cloud computing environment 500 includes one or morecloud computing nodes 510 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 520-1, desktop computer 520-2, laptop computer520-3, and/or automobile computer system 520-4 may communicate. Nodes510 may communicate with one another. They may be grouped (not shown)physically or virtually, in one or more networks, such as Private,Community, Public, or Hybrid clouds as described hereinabove, or acombination thereof. This allows cloud computing environment 500 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 520-1-520-4shown in FIG. 5 are intended to be illustrative only and that computingnodes 510 and cloud computing environment 500 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers 600provided by cloud computing environment 500 (FIG. 5) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 6 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 610 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 611;RISC (Reduced Instruction Set Computer) architecture based servers 612;servers 613; blade servers 614; storage devices 615; and networks andnetworking components 616. In some embodiments, software componentsinclude network application server software 617 and database software618.

Virtualization layer 620 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers621; virtual storage 622; virtual networks 623, including virtualprivate networks; virtual applications and operating systems 624; andvirtual clients 625.

In one example, management layer 630 provides the functions describedbelow. Resource provisioning 631 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 632provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 633 provides access to the cloud computing environment forconsumers and system administrators. Service level management 634provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 635 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 640 provides examples of functionality for which thecloud computing environment can be utilized. Examples of workloads andfunctions that can be provided from this layer include: mapping andnavigation 641; software development and lifecycle management 642;virtual classroom education delivery 643; data analytics processing 644;transaction processing 645; and machine learning for reaction ruledatabase correction 646.

The present disclosure may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent disclosure.

The computer readable storage medium is a tangible device that canretain and store instructions for use by an instruction executiondevice. Examples of computer readable storage media can include anelectronic storage device, a magnetic storage device, an optical storagedevice, an electromagnetic storage device, a semiconductor storagedevice, or any suitable combination of the foregoing. A non-exhaustivelist of more specific examples of the computer readable storage mediumincludes the following: a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a static randomaccess memory (SRAM), a portable compact disc read-only memory (CD-ROM),a digital versatile disk (DVD), a memory stick, a floppy disk, amechanically encoded device such as punch-cards or raised structures ina groove having instructions recorded thereon, and any suitablecombination of the foregoing. A computer readable storage medium, asused herein, is not to be construed as being transitory signals per se,such as radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers, and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a component, segment, orportion of instructions, which comprises one or more executableinstructions for implementing the specified logical function(s). In somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Although the present disclosure has been described in terms of specificembodiments, it is anticipated that alterations and modification thereofwill become apparent to the skilled in the art. Therefore, it isintended that the following claims be interpreted as covering all suchalterations and modifications as fall within the true spirit and scopeof the disclosure.

What is claimed is:
 1. A method of reaction rules database correction,comprising: receiving a first user-input correction to a first reactionrule in a reaction rules database; locating a second reaction rule inthe reaction rules database that is similar to the first reaction rule;calculating a correctness score for the located second reaction rule;determining that the correctness score for the located second reactionrule is below a threshold correctness score; presenting, in response tothe determining that the correctness score for the located secondreaction rule is below the threshold correctness score, the locatedsecond reaction rule to a user; receiving a second user-input correctionto the located second reaction rule; updating the reaction rulesdatabase to include the second user-input correction and learning, basedon the updating, a function for predicting correctness scores foradditional reaction rules in the reaction rules database.
 2. The methodof claim 1, further comprising: locating an additional reaction rule inthe reaction rules database that is similar to the located secondreaction rule; calculating a correctness score for the additionalreaction rule; determining that the correctness score for the additionalreaction rule is below the threshold correctness score; presenting, inresponse to the determining that the correctness score for theadditional reaction rule is below the threshold correctness score, theadditional reaction rule to the user; receiving a user-input correctionto the additional reaction rule; and updating the reaction rulesdatabase to include the user-input correction to the additional reactionrule.
 3. The method of claim 1, further comprising: receiving auser-input confidence level for the second user-input correction;determining that the user-input confidence level is below a thresholdconfidence level; presenting, in response to the determining that theuser-input confidence level is below the threshold confidence level, thelocated second reaction rule to a second user; receiving a response fromthe second user confirming the second user-input correction; andupdating the reaction rules database to include the confirmation fromthe second user.
 4. The method of claim 1, wherein the presenting thelocated second reaction rule includes automatically opening a new windowin a user interface of a computer-assisted synthetic design program. 5.The method of claim 1, further comprising generating an alert upondetermining that the correctness score for the located second reactionrule is below the threshold correctness score.
 6. The method of claim 1,wherein the first reaction rule and the located second reaction ruleinclude incorrect bonds.
 7. The method of claim 1, wherein thecorrectness score is a measure of how similar the located secondreaction rule is to the first reaction rule.
 8. The method of claim 1,wherein the reaction rules database includes reaction rules extractedfrom at least one source selected from a group consisting of at leastone scientific article, at least one book, and at least one patent.
 9. Asystem, comprising: at least one processing component; at least onememory component; a user interface; a reaction rules database; and areaction rules correction module, comprising: a machine learningcomponent, executing on the at least one processing component,configured to: receive a first user-input correction to a first reactionrule in the reaction rules database; locate a second reaction rule inthe reaction rules database that is similar to the first reaction rule;calculate a correctness score for the located second reaction rule;determine that the correctness score for the located second reactionrule is below a threshold correctness score; present, in response to thecorrectness score being below the threshold correctness score, thelocated second reaction rule to a user, wherein the located secondreaction rule is displayed on the user interface; receive a seconduser-input correction to the located second reaction rule; update thereaction rules database to include the second user-input correction tothe located second reaction rule; and based on the update, learn afunction for predicting correctness scores for additional reaction rulesin the reaction rules database.
 10. The system of claim 9, wherein themachine learning component is further configured to: receive auser-input confidence level for the second user-input correction;determine that the user-input confidence level is below a thresholdconfidence level; present, in response to the user-input confidencelevel being below the threshold confidence level, the located secondreaction rule to a second user; receive a response from the second userconfirming the second user-input correction; and update the reactionrules database to include the confirmation from the second user.
 11. Thesystem of claim 9, wherein the machine learning component is furtherconfigured to generate an alert upon determining that the correctnessscore for the located second reaction rule being below the thresholdcorrectness score.
 12. The system of claim 9, wherein the reaction rulescorrection module further comprises a retrosynthetic analysis component.13. The system of claim 9, wherein the first reaction rule and thelocated second reaction rule include incorrect bonds.
 14. The system ofclaim 9, wherein the correctness score for the located second reactionrule is a measure of how similar the located second reaction rule is tothe first reaction rule.
 15. A computer program product for data storagemanagement, the computer program product comprising a computer readablestorage medium having program instructions embodied therewith, theprogram instructions executable by a processor to cause the device toperform a method, the method comprising: receiving a first user-inputcorrection to a first reaction rule in a reaction rules database;locating a second reaction rule in the reaction rules database that issimilar to the first reaction rule; calculating a correctness score forthe located second reaction rule; determining that the correctness scorefor the located second reaction rule is below a threshold correctnessscore; presenting, in response to the determining that the correctnessscore for the located second reaction rule is below a thresholdcorrectness score, the located second reaction rule to a user; receivinga second user-input correction to the second reaction rule; updating thereaction rules database to include the second user-input correction tothe located second reaction rule; and learning, based on the updating, afunction for predicting correctness scores for additional reaction rulesin the reaction rules database.
 16. The computer program product ofclaim 15, further comprising: locating an additional reaction rule inthe reaction rules database that is similar to the located secondreaction rule; calculating a correctness score for the additionalreaction rule; determining that the correctness score for the additionalreaction rule is below the threshold correctness score; presenting, inresponse to the determining that the correctness score for theadditional reaction rule is below the threshold correctness score, theadditional reaction rule to the user; receiving a user-input correctionto the additional reaction rule; and updating the reaction rulesdatabase to include the user-input correction to the additional reactionrule.
 17. The computer program product of claim 15, further comprising:receiving a user-input confidence level for the located seconduser-input correction; determining that the user-input confidence levelis below a threshold confidence level; presenting, in response to thedetermining that the user-input confidence level is below the thresholdconfidence level, the located second reaction rule to a second user;receiving a response from the second user confirming the seconduser-input correction; and updating the reaction rules database toinclude the confirmation from the second user.
 18. The computer programproduct of claim 15, wherein the presenting the located second reactionrule includes automatically opening a new window in a user interface ofa computer-assisted synthetic design program user interface.
 19. Thecomputer program product of claim 15, further comprising generating analert upon determining that the correctness score is below the thresholdcorrectness score.
 20. The computer program product of claim 15, whereinthe first reaction rule and the located second reaction rule includeincorrect bonds.